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In this paper, 22 indexes are selected at three levels, including the informatization development level, the Internet development level, and the digital transaction development level, based on China's provincial panel data from 2011 to 2020, so as to build a digital economy development index system. Moreover, 28 basic indexes are selected from three aspects, including energy construction, energy production and energy consumption, so as to develop an energy economy development evaluation index system. The development index of China's digital economy and energy economy are measured by using the entropy weight method. The effect of the digital economy on the energy economy and its mechanism are tested by the static panel, the dynamic panel, and the mediating effect and regulating effect models. The results indicate that the digital economy has pronouncedly promoted the development of China's energy economy, and the development of the digital economy can have an effect on the rationalization of the industrial structure and then affect the development of the energy economy, and there is an intermediary effect. Moreover, the upgrading of the industrial structure is conducive to regulating the digital economy and facilitates the development of the energy economy. The development of the energy economy can be better promoted by focusing on the coordinated regional layout of the digital economy development, building a reliable energy commodity trading platform, and expediting the optimization and upgrading of the industrial structure. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to reveal the mechanism and process of the dynamic evolution of the economy-energy-environment (EEE) system and study the variables that determine the speed of system evolution and development. The innovation is to overcome the defect of single index by constructing a system evaluation index and reveal the regional EEE system's evolution mechanism based on Haken's synergetic theory to supplement a systematic perspective's empirical evidence. It selects 30 provinces, municipalities, and autonomous regions in China to empirically analyse the evolution mechanism of the EEE system from 2005 to 2019. The empirical results show that economic development is the order parameter of the evolution and development of the EEE system. In the process of system evolution, the economic development subsystem is the decisive factor in EEE system evolution. In terms of the positive feedback mechanism of order parameter combination, the EEE system forms a positive feedback mechanism for increasing economic development in the stage of evolution and development. The negative feedback mechanism of decreasing environmental pollution emissions is formed in the EEE system. The increase in pollutant emissions has a certain inhibitory effect on economic growth. The improvement in the economic development level has no promoting effect on pollutants, and economic development, in turn, reduces environmental pollution. [ABSTRACT FROM AUTHOR]

The rapid development of digital finance has delivered significant benefits, such as sustainable development and economic growth. We explore the relationship between digital finance and green total factor energy efficiency (GTFEE) for the first time, filling a gap in the existing literature. This paper uses dynamic panel models to explore digital finance's impact on GTFEE at the Chinese city-level panel data from 2011 to 2018. The results show that digital finance can significantly improve urban GTFEE, and the findings remain robust with various tests. Second, the mechanism analysis indicates that digital finance can improve GTFEE by promoting urban green technology innovation and industrial structure upgrading. Further study shows that digital finance has a better effect on the improvement of GTFEE in central and western cities, small cities and non-resource-based cities, but has no significant or small impact on GTFEE in eastern cities, large cities and resource-based cities, reflecting the inclusiveness of digital finance. [ABSTRACT FROM AUTHOR]

Green innovation possesses dual externalities of "innovation" and "environmental protection", and enhancing energy efficiency serves as a crucial means to promote high-quality economic development. Building upon the energy rebound effect, we use the balanced panel data of cities at prefecture level and above in China from 2008 to 2018 to explore the impact of urban green innovation on total factor energy efficiency (TFEE). The findings of this study indicate that, firstly, the impact of green innovation on TFEE exhibits a positive U-shaped pattern, characterized by initial suppression followed by subsequent promotion. This conclusion remains robust after undergoing a series of rigorous robustness tests. Second, high-quality green innovation is found to reach the turning point more quickly, implying that substantial green innovation can cross the turning point in smaller quantities. Thirdly, on the whole, in comparison to non-resource-based cities, resource-based cities are capable of reaching the turning point at an earlier stage. Finally, the new energy demonstration cities have not yet reached the turning point, while the non-new energy demonstration cities have crossed the turning point. This study contributes novel insights into the relationship between green innovation and TFEE, which holds significant implications for the formulation and implementation of sustainable development policies. [ABSTRACT FROM AUTHOR]

There are differences in economic level, population size, and technical level in different stages. At the same time, as a coal-dependent country, other regions have foreign dependence on coal. Then the primary factors and influence degrees of CO2 emissions in different stages and areas with different coal dependents will be various. This paper adds urbanization rate, industrial structure, and energy consumption structure to the traditional STIRPAT model. Based on the expanded STIRPAT model, the panel data of 30 regions in China from 2005 to 2019 are used to analyze the different stages and areas with foreign coal dependence. The main influencing factors and degree of influence of CO2 emissions. Regression finds that population size, energy consumption, and industrial structure will increase regional carbon dioxide emissions. There is an "inverted U-shaped" curve relationship between economic development, technological level, and carbon dioxide emissions. The improvement of the urbanization rate can reduce carbon dioxide emissions. Carbon dioxide emissions. An "inverted U-shaped" curve relationship between per capita GDP and CO2 emissions in three research periods from 2005 to 2010, 2011 to 2015, and 2016 to 2019. At the same time, the inhibition effect of the urbanization rate is constantly strengthened, and the promotion effect of the coal consumption ratio is decreasing year by year. The influence of population size, technical level, and industrial structure on carbon dioxide emissions in the whole country and areas with different coal dependents varies with the study period. To provide a theoretical basis for local conditions and regional management in the future direction of energy-saving and carbon reduction. [ABSTRACT FROM AUTHOR]

Based on the panel data of 227 prefecture-level and above cities in China from 2002 to 2018, a panel linkage equation model is constructed to explore the bidirectional influence relationship between haze pollution and urban sprawl, and the results of the study find that, firstly, there is a bidirectional promotion of causality between haze pollution and urban sprawl. That is, PM2.5 not only has a significant positive effect on urban sprawl, but also urban sprawl has a significant positive correlation with haze pollution, which is further strengthened by adding the air flow coefficient instrumental variable. Second, the heterogeneity analysis yields that haze pollution has different effects on urban sprawl in different regions. Under the sub-regional samples, haze pollution and urban sprawl have a bi-directional significant negative impact relationship in the eastern region, none of the haze pollution and urban sprawl have a bi-directional significant impact relationship in the western region, but both the central region and the northeastern region have a significant positive impact relationship. Under different city sizes, haze pollution and urban sprawl in large, medium and small cities have a bi-directional significant positive impact relationship, and from the numerical size, the degree of influence of haze pollution on urban sprawl in large cities is greater than that in small and medium cities; while the degree of influence of urban sprawl on haze pollution in medium cities is greater than that in large and small cities. Accordingly, it is proposed that urban governance should be adapted to local conditions, focus on innovative technologies to reduce energy consumption, and utilize big data to manage cities. [ABSTRACT FROM AUTHOR]

With the continuous uptick in world energy consumption, green energy plays an increasingly significant role in alleviating energy depletion and promoting economic development. Due to regional differences, transportation restrictions, national policies and other reasons, there is a mismatch between green energy needs and resources across regions in China, which leads to misallocation of capital and labor between heavily polluting and nonpolluting enterprises. In this context, this paper first proposes a new measurement method: namely, the capital and labor mismatch index. Panel data of 30 Chinese provinces from 2008 to 2018 are utilized to calculate the index. The results show significant resource mismatch across provinces in China. In addition, we extend the green Solow model by combining capital mismatch, technological progress and environmental regulation into a dynamic analysis framework of economic growth. The empirical results show that all three factors have significant spatial spillover effects. Specifically, capital misallocation exerts a strong negative impact on economic growth, while the other factors increase it. [ABSTRACT FROM AUTHOR]

Introduction: The creation of infrastructure in rural areas is an essential guarantee for the economic development and in improving farmers' living standards. As a critical link between urban and rural production and consumption, rural logistics is essential to maximize the efficiency of rural infrastructure investment. Methods: Based on the panel data of 31 provinces in China from 2011-2020, the study analyzes the impact of high-quality development of rural logistics on the enhancement of residents' consumption in terms of scale and quality, using a fixed-effects model. Results: It suggest that this development promotes the growth of rural residents' consumption and quality, with a noticeable regional heterogeneity. The most significant effect is observed in the eastern region. Further analysis shows that inland and less developed provinces of rural e-commerce stimulate residents' consumption upgrade with the help of high-quality development of rural logistics at a level weaker than coastal provinces and developed provinces of rural e-commerce. Discussion: Consequently, it is recommended to boost the building of logistics infrastructure in rural areas and actively cultivate diversified rural logistics operators in the western and northeastern regions. The contribution of this study is to examine the impact of rural logistics on rural residents' consumption from the perspective of infrastructure, further validating the role of infrastructure development in promoting economic growth. [ABSTRACT FROM AUTHOR]

Using panel data of 11 provinces in the western region of China, the paper empirically analyzes the influence of public environmental participation on the effectiveness of pollution governance. Based on documents from provincial governments in Western China, the paper uses Nvivo software to extract keywords about local governments' attention to the environment and examines its mediating effect between public environmental participation and pollution governance. Furthermore, a classifiable analysis of government environmental attention is carried out, and the effect of local governments' attention to the environment on pollution governance is studied. The results show that public environmental participation promotes pollution governance by increasing the governments' attention to the environment. Additionally, local governments' environmental attention also has a promotive effect on pollution governance, and the increase in environmental decision-makers' attention to environmental pollution and energy consumption is conducive to the improvement of environmental quality, but the effect of environmental protection and collaborative attention on environmental quality improvement still needs to be explored further. • Local governments' attention to the environment is estimated. • Public environmental participation promotes pollution governance. • Local Governments' Environmental Attention plays mediating role. • Local Governments' Environmental Attention has a promotive effect on environmental quality. • The effect of collaborative attention on environmental quality improvement still needs to be explored further. [ABSTRACT FROM AUTHOR]

This paper has used a stirpat model to investigate the synergistic effect of CO2 emission, energy consumption, energy intensity, economic growth, population, urbanization and trade openness to demonstrate growthenvironment nexus in four selected developing Asian economies. Taking a panel data set from Bangladesh, China, India and Indonesia this study applies Autoregressive Distributed Lag (ardl) model and vec Granger Causality/Block Exogeneity Wald Tests. The empirical results show that energy intensity, urbanization, population, and per capita gdp growth are the raison d'être of CO2 emissions whereas trade openness is found to be negatively related to CO2 emissions. Conversely, energy consumption, urbanization, population and trade openness are positively related to per capita gdp. In addition, it also investigates the Environmental Kuznets Curve (EKC) hypothesis and the findings substantiate an inverted Ushaped relationship. Cross-section short-run coefficients of country-level data are inquired into to check the robustness of the panel outcomes. [ABSTRACT FROM AUTHOR]

To gain a deeper understanding of the carbon emission mechanism from transportation facilities, all system elements affecting carbon emissions from regional transportation facilities are identified and analyzed according to panel data from 30 regions in China. A spatial econometric model for carbon emissions from transportation facilities is constructed using the Spatial Dolbin model from 2004 to 2022 as the research period. From the results, the carbon dioxide emissions from transportation facilities added from 318 million tons in 2004 to 752 million tons in 2022, with an average annual growth rate of 4.9%. The global spatial auto-correlation coefficient was significant at the 5%, with an obvious spatial correlation between carbon dioxide emissions within a geographical range. In addition, through stability testing, the model showed high stability in both spatial lag testing and spatial error testing, demonstrating strong ability to interpret data. The research shows that the carbon emission is affected by independent variables, including population, economy, technology, and transportation, and exhibit significant spatial distribution characteristics in different regions and years, providing a basis for policy formulation and carbon emission management. [ABSTRACT FROM AUTHOR]

Ecological deterioration, air pollution, and resource depletion have shrouded the vast regions of China, raising widespread concerns about the sustainable development of the domestic economy. Although financial agglomeration has become a pivotal approach for China to realize green transformation, there is a lack of evidence against the causal correlation between financial agglomeration and sustainable development of the regional economy. To fill this gap, using the data of 29 provincial capital cities in China spanning from 2009 to 2019 and adopting individual time bidirectional fixed effect model, IV-GMM approach, and alternative modeling techniques, this paper investigates the impact of financial agglomeration on sustainable development of the regional economy for the first time. The results indicate that there is a significant positive correlation between financial agglomeration and the sustainable development of the regional economy. Financial agglomeration facilitates the improvement of regional energy efficiency, and the latter further mediates the relationship between financial agglomeration and sustainable development of the regional economy. In addition, the empirical results also demonstrate that the higher the economic policy uncertainty, the weaker the positive relationship between financial agglomeration and energy efficiency. The present study is of great significance for China to implement energy-saving and emission-reduction tasks and achieve sustainable urban construction. [ABSTRACT FROM AUTHOR]

With the panel data of 21 China's industrial industries from 2008 to 2020, the relationship models between intelligent industry, energy regulation and ecological transformation are constructed and tested from two dimensions of resource saving and environmental friendliness, then equity financing is introduced into this model as moderating variable to discuss the moderating effects on the relationships between intelligent industry, energy regulation and ecological transformation. Results show that: ⑴China's industrial industries significantly transformed to the resource-saving type, and the environment-friendly level stayed in a slow progression. ⑵Intelligent industry affected ecological transformation positively and significantly. The impact of energy regulation on ecological transformation was nonlinear. The regulation of energy consumption can significantly stimulate the transformation of resource saving, and restrain the transformation of environmental friendliness; the regulation of energy structure can significantly stimulate the transformation of environmental friendliness. ⑶ Equity financing can positively moderate the relationship between intelligent industry and ecological transformation, and it can also moderate the regulation of energy structure and promote the transformation to environmental friendliness, especially in the low consumption industries. [ABSTRACT FROM AUTHOR]

One of the critical challenges facing China is environmental pollution caused by insufficient use of clean energy. This paper empirically investigates the impact of rural labor mobility on household clean energy use using data from the China Family Panel Studies (CFPS). The results indicate that labor mobility significantly promotes the adoption of clean energy by rural households, with income effects and information channel effects serving as the underlying mechanisms. The study also finds that health risks, as an important consideration for household energy use choices, enhance this positive effect. Furthermore, heterogeneity analysis reveals that labor mobility has a greater impact on households with lower levels of education, lack of social security, and those located in central and western regions. Therefore, the government should take measures to increase the income of the rural migrant population and continuously widen the information channels for rural households to promote clean energy use. The findings of this study provide a new perspective and evidence for understanding household energy use behavior in China, offering valuable insights for policymakers aiming to expand the use of clean energy. • Labour mobility significantly contributes to clean energy use in rural households. • Income effects and information channel effects are the main mechanisms of action. • Health risks faced by households play a moderating role. • Heterogeneity in region, educational attainment, social security status will influence the effect of labour mobility. [ABSTRACT FROM AUTHOR]

Human activities are closely related to energy consumption and carbon emissions. Nevertheless, in the context of energy transition and carbon emission reduction policies, the impact of population migration on energy consumption and carbon emissions is still unclear. Based on the panel data of 30 provincial regions in China from 2000 to 2019, this paper uses the spatial Durbin model to perform a spatial econometric investigation on this issue. In particular, this study innovatively embeds two spatial weight matrices, an economic gravity weight matrix and a population migration weight matrix, into the spatial Durbin model to capture the spatial spillover effects of population migration. The results show that population migration has obvious adverse impacts on the provinces with net outward population migration. Specifically, on the one hand, population migration increases energy consumption and energy poverty, but energy poverty is more severe in provinces with net outward population migration. On the other hand, population migration increases the carbon emissions and carbon reduction barriers of the provinces with net outward population migration and has no significant impact on the carbon emissions and carbon reduction barriers of the provinces with net inward population migration. Besides, the results of heterogeneity analysis show that population migration causes gaps in the energy structure among regions and even triggers the carbon transfer to provinces with net outward population migration. On the whole, the research results of this paper all prove that population migration increases the carbon reduction barriers in the provinces with net outward population migration. These empirical insights enrich the relevant theoretical achievements of the impact of population migration on energy consumption and carbon emissions and have a high reference value for formulating more reasonable regional energy transition and carbon emission reduction policies. • The spatial spillover effects of population migration on energy consumption and carbon emissions are explored. • The spatial Durbin model is used in this paper. • The spatial Durbin model is embedded with the economic gravity weight matrix and the population migration weight matrix. • Population migration has obvious adverse impacts on the provinces with net outward population migration [ABSTRACT FROM AUTHOR]

• The impact of economic growth pressure on energy efficiency improvement is discussed. • Economic growth pressure has hindered the improvement of energy efficiency. • Heterogeneity effects exist in individual dimension and temporal dimension. Economic growth target management has been a typical feature of Chinese governments at all levels for a long time in the past decades. Based on a panel data of 188 Chinese cities, this paper examines the impact of pressure brought by economic growth target on energy efficiency improvement. The results suggest that economic growth pressure has hindered the improvement of energy efficiency. For every unit of increase in economic growth pressure, the improvement indicator of energy efficiency will decrease by 3.3%. This finding is robust, after replacing explained variables and core explanatory variables, no substantial changes are found. Sub-sample regression confirms the existence of heterogeneity effects in individual dimension and temporal dimension. Finally, by summarizing existing studies, this paper finds that the investment imbalance effect, the hindrance effect on industrial structure upgrading, and the crowding-out effect on innovation are three important ways for economic growth pressure to restrain the improvement of energy efficiency. Our findings are helpful to understand the role of the government's economic growth target management in resource allocation and energy utilization and provide a certain practical reference for China to promote high-quality economic development. [ABSTRACT FROM AUTHOR]

China's carbon emissions have a stable industrial concentration. In recent years, the carbon emissions of the six major high-carbon industries have accounted for approximately 80% of the national total and are thus priority areas for emission reduction. With the promotion of energy-saving and emission-reduction policies, the structure and scale of high-carbon industries in various regions have undergone changes, but their carbon reduction effects show significant regional differences. Based on China's provincial panel data from 2006 to 2020, this study discusses the structural characteristics of high-carbon industries with their proportion of energy-based industries and measures their scale characteristics with their output values. On this basis, a fixed-effects model is used to analyze the single and synergistic effects of the scale and structure of high-carbon industries on carbon emissions in each province. The results indicate that changes in the scale and structure of high-carbon industries significantly affect carbon emissions but show regional differences in both the single and synergistic effects. When considering these synergistic effects, the single effect of high-carbon industries on carbon emissions will be weakened. In regions with large-scale high-carbon industries, the increase in the proportion of energy-based industries significantly increases carbon emissions, but this effect gradually weakens as the overall scale expands. In areas with small-scale high-carbon industries, the increase in the proportion of energy-based industries has a relatively small effect on carbon emission growth that gradually increases with the overall scale. In addition, the implementation of the carbon emission trading policy has a significant moderating effect on the carbon emissions of high-carbon industries and strongly promotes its reduction. [ABSTRACT FROM AUTHOR]

For economic activities, energy is a necessary component of production. As the world's largest energy consumer and carbon emitter, China faces greater resource constraints and environmental pressure in the background of global climate change, improving energy efficiency is the inevitable path for long-term development. However, the energy efficiency in China at this stage is relatively low, thus improving energy efficiency is urgent. Based on the panel data from the database of Chinese industrial enterprises and the database of Chinese industrial enterprises' pollutant emissions between 2003 and 2012, this paper empirically conducts a thorough investigation on the nonlinear impact of spatial agglomeration and enterprises' energy efficiency, and further examine its heterogeneity and mechanisms. The results show that: (1) There is a significant U-shaped link between spatial agglomeration and enterprises' energy efficiency. (2) Based on agglomeration externalities, scale effect, learning effect, competition effect, and policy effect are the internal mechanisms through which spatial agglomeration impacts on enterprises' energy efficiency. (3) The effects of spatial agglomeration on enterprises' energy efficiency are influenced by technology level, energy consumption level, agglomeration patterns, and enterprise characteristics. Based on the empirical findings, we suggest that enterprises take full advantage of the benefits brought by spatial agglomeration, and that the government encourage the growth period of spatial agglomeration to become more profound, more synergetic, and more diverse. [ABSTRACT FROM AUTHOR]

The goal of this study is to develop a model based on panel data from 30 Chinese provinces to assess rebound and fixed effects, as well as policy implications, in order to provide a theoretical foundation for the rapid advancement of lowcarbon transformation, which is a necessary result of a new stage in environmental growth. The results of primary analysis are the CO2 emission intensity reached 15.72 kt/million yuan in 2019, which was an great decline of 21.86 kt/million yuan as compared with the intensity in 2001; the average value of technological progress reached 1.017, waved like a U shape. The findings of empirical study show that the amount of energy CO2 emission rebound ranged widely; the average value of the rebound impact was -0.449; a 1% rise of technological progress led a 0.442% CO2 emission decrease, notably in Central and Western China. The results lower the cost of drafting energy policies as well as the decision-making involved and increase the economic advantages and transformation rate of technical breakthroughs in practice. [ABSTRACT FROM AUTHOR]

Following a dynamic nonlinear perspective, this study explores the relationship between urban innovation capability and energy utilization efficiency by employing the Panel Vector Autoregression (PVAR) and Dynamic Panel Threshold Regression (DPTR) methods. Using the 2003–2020 panel data of 281 prefecture-level cities in China, this study confirms that energy utilization efficiency improves owing to the improvement of urban innovation capability. Depending on the characteristics of the city, such as population density, industrial structure, and environmental pollution, high energy utilization efficiency in the early stages of city development may help or hinder the improvement of energy utilization efficiency in the later stages. The enhancement in urban innovation capability has failed to improve energy utilization efficiency and has adversely affected cities with a low population density or weak secondary industrial foundation. However, in cities with a high population density or proportion of secondary industry, the improvement in innovation capability significantly increases the efficiency of energy utilization. In addition, the positive effect that urban innovation capability has on energy utilization efficiency is higher in low-pollution cities than in high-pollution cities. [ABSTRACT FROM AUTHOR]

The mechanism between financial agglomeration and environmental pollution is an important concern for both academia and policymaking. The main objective of this paper is to study the nonlinear impacts of financial agglomeration on environmental pollution. A theoretical framework was first constructed based on the scale effect, structure effect, and technology innovation effect of financial agglomeration and a Copeland-Taylor endogenous growth model. Using the panel data of 281 Chinese prefectural-level cities from 2003 to 2019, a panel threshold regression model was introduced to estimate the nonlinear association between financial agglomeration and environmental pollution. Industrial smoke (dust) emissions and industrial wastewater discharge were adopted to quantify current environmental pollution in China. The results show that financial agglomeration had a significant effect on improving the environment characterized by gradient thresholds; also notable is that 68.64% of the cities crossed the threshold value, entering the decelerating phase of financial agglomeration inhibiting environmental pollution. Both upgrading industrial structure and enhancing marketization could reduce environmental pollution, whereas increasing human capital, environmental regulation, and energy consumption had a deteriorating effect. The three channels for financial agglomeration to reduce environmental pollution were revealed to be financial scale, financial structure, and financial technology innovation. Our findings provide strong evidence for policymaking in sustainable development. [Display omitted] • Financial agglomeration (FA) is a crucial way to improve environment pollution (EP). • We built the theoretical framework of FA and EP based on the Copeland-Taylor model. • We tested the nonlinear impacts of FA on EP using the panel threshold model. • We revealed the transmission mechanism between FA and EP. [ABSTRACT FROM AUTHOR]

• Assessing the Environmental Performance of china's Three Urban Agglomerations. • Using the dynamic DEA model based on the input–output perspective. • The total environmental efficiency exhibits a wave-like development trend. • PM2.5 is the best unexpected output after strict prevention from 2013 to 2017. • Industrial smoke and dust emissions need to strengthen management in the future. As a special form of a cluster city, urban agglomeration has become an important platform to support national economic growth and to participate in international competition and cooperation, but it faces severe environmental problems. This research thus selects panel data of 60 cities in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) from 2013 to 2017. Based on the input–output perspective, labor and energy consumption per GDP are selected as input indicators, GDP, PM2.5, SO 2 , industrial smoke and dust emissions, and wastewater discharged as output indicators, and fixed assets as a carry-over variable in the dynamic DEA model to evaluate and analyze the environmental efficiencies of these three urban agglomerations. Findings show that their total environmental efficiency exhibits a wave-like development trend, and that the total efficiency score of BTH is better than that of PRD and YRD. From the viewpoint of a sub-efficiency index, PM2.5 is the lowest in unexpected output, where its average five-year efficiency score is 0.9847, while the average score of industrial smoke and dust emissions efficiency is the lowest at 0.6199. Finally, this paper offers suggestions for different problems facing the three urban agglomerations. [ABSTRACT FROM AUTHOR]

• We estimate the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. • The average value of efficiency is 0.491, and the average rebound effect is 48%. • We find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. • The rebound effect varies across before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. The rebound effect is a potential threat to energy efficiency related policies to save energy. Based on data from China family panel studies over the period 2010–2018, this paper measures the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. The result shows that the average value of efficiency is 0.491, and the average rebound effect is 48%. It means that there is still room for improvement in China's household electricity consumption efficiency. But the expected electricity- saving after improving efficiency can only achieve 52%. Besides, we find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. Finally, the rebound effect varies between before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. These findings provide some policy implications for the residential electricity pricing system under the background of energy conservation and climate change. [ABSTRACT FROM AUTHOR]